{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/swap-253/Time-Series-And-Anomaly-Detection/blob/main/Anomaly_detection_Using_LSTM_AutoEncoders.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "PbVkw3i7mg0Z"
   },
   "outputs": [],
   "source": [
    "#importing suitable libraries\n",
    "import numpy as np\n",
    "import tensorflow.compat.v2 as tf\n",
    "tf.enable_v2_behavior()\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from pylab import rcParams\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rc\n",
    "from pandas.plotting import register_matplotlib_converters\n",
    "import plotly.graph_objects as go\n",
    "import tensorflow_datasets as tfds\n",
    "import tensorflow_probability as tfp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "swXNFaQ5Csoo"
   },
   "outputs": [],
   "source": [
    "#setting sns and plotly plots\n",
    "sns.set(style='whitegrid', palette='muted', font_scale=1.5)\n",
    "rcParams['figure.figsize'] = 10,5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 72,
     "resources": {
      "http://localhost:8080/nbextensions/google.colab/files.js": {
       "data": 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       "headers": [
        [
         "content-type",
         "application/javascript"
        ]
       ],
       "ok": true,
       "status": 200,
       "status_text": ""
      }
     }
    },
    "id": "x-Yd0mc6zVb0",
    "outputId": "6b803744-ea48-4b51-d5b2-88bea7d5138c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "     <input type=\"file\" id=\"files-8e05c6b3-0f71-4687-b5da-e4395a34a3d9\" name=\"files[]\" multiple disabled\n",
       "        style=\"border:none\" />\n",
       "     <output id=\"result-8e05c6b3-0f71-4687-b5da-e4395a34a3d9\">\n",
       "      Upload widget is only available when the cell has been executed in the\n",
       "      current browser session. Please rerun this cell to enable.\n",
       "      </output>\n",
       "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving JNJ.txt to JNJ.txt\n"
     ]
    }
   ],
   "source": [
    "from google.colab import files\n",
    "uploaded = files.upload()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "dhcfLdSHm6p-",
    "outputId": "301011b2-e86b-4adb-d953-1cdd47f1d1e6"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1985-09-04</td>\n",
       "      <td>2.937500</td>\n",
       "      <td>2.960938</td>\n",
       "      <td>2.929688</td>\n",
       "      <td>2.953125</td>\n",
       "      <td>0.182094</td>\n",
       "      <td>4188800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1985-09-05</td>\n",
       "      <td>2.953125</td>\n",
       "      <td>2.992188</td>\n",
       "      <td>2.945313</td>\n",
       "      <td>2.976563</td>\n",
       "      <td>0.183539</td>\n",
       "      <td>6176000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1985-09-06</td>\n",
       "      <td>2.992188</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.976563</td>\n",
       "      <td>2.992188</td>\n",
       "      <td>0.184502</td>\n",
       "      <td>6606400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1985-09-09</td>\n",
       "      <td>2.992188</td>\n",
       "      <td>2.992188</td>\n",
       "      <td>2.945313</td>\n",
       "      <td>2.945313</td>\n",
       "      <td>0.181612</td>\n",
       "      <td>3150400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1985-09-10</td>\n",
       "      <td>2.953125</td>\n",
       "      <td>2.960938</td>\n",
       "      <td>2.867188</td>\n",
       "      <td>2.875000</td>\n",
       "      <td>0.177276</td>\n",
       "      <td>2841600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date      Open      High       Low     Close  Adj Close   Volume\n",
       "0  1985-09-04  2.937500  2.960938  2.929688  2.953125   0.182094  4188800\n",
       "1  1985-09-05  2.953125  2.992188  2.945313  2.976563   0.183539  6176000\n",
       "2  1985-09-06  2.992188  3.000000  2.976563  2.992188   0.184502  6606400\n",
       "3  1985-09-09  2.992188  2.992188  2.945313  2.945313   0.181612  3150400\n",
       "4  1985-09-10  2.953125  2.960938  2.867188  2.875000   0.177276  2841600"
      ]
     },
     "execution_count": 143,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reading the dataframe\n",
    "df = pd.read_csv('JNJ.txt')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "-Pfl0g3QzvSy"
   },
   "outputs": [],
   "source": [
    "#selecting useful columns from the dataframe\n",
    "df = df[['Date', 'Close']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "NvTljh07WMAE",
    "outputId": "f0923f62-fd01-4058-b097-c651982bbc1d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8818</th>\n",
       "      <td>2020-08-28</td>\n",
       "      <td>153.639999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8819</th>\n",
       "      <td>2020-08-31</td>\n",
       "      <td>153.410004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8820</th>\n",
       "      <td>2020-09-01</td>\n",
       "      <td>151.520004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8821</th>\n",
       "      <td>2020-09-02</td>\n",
       "      <td>153.830002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8822</th>\n",
       "      <td>2020-09-03</td>\n",
       "      <td>149.550003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date       Close\n",
       "8818  2020-08-28  153.639999\n",
       "8819  2020-08-31  153.410004\n",
       "8820  2020-09-01  151.520004\n",
       "8821  2020-09-02  153.830002\n",
       "8822  2020-09-03  149.550003"
      ]
     },
     "execution_count": 145,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#just to see that the last date on which data was collected is 3 September 2020\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "4oQdQpuP2Hb2",
    "outputId": "b7fdd40b-7a6a-4292-e97a-b222b6a10c72"
   },
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1985-09-04</td>\n",
       "      <td>2.953125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1985-09-05</td>\n",
       "      <td>2.976563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1985-09-06</td>\n",
       "      <td>2.992188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1985-09-09</td>\n",
       "      <td>2.945313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1985-09-10</td>\n",
       "      <td>2.875000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date     Close\n",
       "0 1985-09-04  2.953125\n",
       "1 1985-09-05  2.976563\n",
       "2 1985-09-06  2.992188\n",
       "3 1985-09-09  2.945313\n",
       "4 1985-09-10  2.875000"
      ]
     },
     "execution_count": 147,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#bringing date in the required format\n",
    "df['Date'] = pd.to_datetime(df['Date'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 542
    },
    "id": "sGH7w4nKikFp",
    "outputId": "43270952-50a3-4956-a0f5-6b6064b8d184"
   },
   "outputs": [
    {
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\"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Johnson and Johnson Stock Price 1985-2020\"}},\n",
       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('ffbbded1-8107-4288-8445-9ce698d63be4');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                \n",
       "            </script>\n",
       "        </div>\n",
       "</body>\n",
       "</html>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#A scatterplot of date vs Closing Price of Johnson and Johnson stock \n",
    "fig = go.Figure()\n",
    "fig.add_trace(go.Scatter(x=df['Date'], y=df['Close'], name='Close price'))\n",
    "fig.update_layout(showlegend=True, title='Johnson and Johnson Stock Price 1985-2020')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "9Cz4C73TxG_Y"
   },
   "outputs": [],
   "source": [
    "#choosing the training and test sets specified by date\n",
    "train, test = df.loc[df['Date'] <= '2013-09-03'], df.loc[df['Date'] > '2013-09-03']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2Qpy9IyKTmju",
    "outputId": "ae87e783-11cd-44c4-f7ae-171396b5d0fa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7059, 1764)"
      ]
     },
     "execution_count": 187,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train),len(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "rRtbidKn2sdr",
    "outputId": "d4d25d53-8001-4ad4-8c68-f7f550112f48"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7054</th>\n",
       "      <td>2013-08-27</td>\n",
       "      <td>86.169998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7055</th>\n",
       "      <td>2013-08-28</td>\n",
       "      <td>86.529999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7056</th>\n",
       "      <td>2013-08-29</td>\n",
       "      <td>86.570000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7057</th>\n",
       "      <td>2013-08-30</td>\n",
       "      <td>86.410004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7058</th>\n",
       "      <td>2013-09-03</td>\n",
       "      <td>86.419998</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date      Close\n",
       "7054 2013-08-27  86.169998\n",
       "7055 2013-08-28  86.529999\n",
       "7056 2013-08-29  86.570000\n",
       "7057 2013-08-30  86.410004\n",
       "7058 2013-09-03  86.419998"
      ]
     },
     "execution_count": 188,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hkxx7ETZ8oF8",
    "outputId": "05b40846-527d-4b56-ad98-331db5e3b412"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:5: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "#for scaling data is fitted on training set and transformed for both training and test sets\n",
    "scaler = scaler.fit(train[['Close']])\n",
    "train['Close'] = scaler.transform(train[['Close']])\n",
    "test['Close'] = scaler.transform(test[['Close']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "rp89-jBZpskM"
   },
   "outputs": [],
   "source": [
    "#A function to create datasets taking 30 closing prices at a time to predict the next closing price\n",
    "def create_dataset(X, y, time_steps=30):\n",
    "    Xs, ys = [], []\n",
    "    for i in range(len(X) - time_steps):\n",
    "        v = X.iloc[i:(i + time_steps)].values\n",
    "        Xs.append(v)        \n",
    "        ys.append(y.iloc[i + time_steps])\n",
    "    return np.array(Xs), np.array(ys)\n",
    "X_train, y_train = create_dataset(train[['Close']], train['Close'])\n",
    "X_test, y_test = create_dataset(test[['Close']], test['Close'])    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UFpUz7OFps8J",
    "outputId": "99d67bb7-0e57-4395-e7d4-f9c80a798104"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7029, 30, 1) (7029,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape,y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "da55zoY1qSQ0",
    "outputId": "c1bbef01-34e5-4ed7-9326-95ce1309d287"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_18\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_35 (LSTM)               (None, 128)               66560     \n",
      "_________________________________________________________________\n",
      "dropout_35 (Dropout)         (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "repeat_vector_18 (RepeatVect (None, 30, 128)           0         \n",
      "_________________________________________________________________\n",
      "lstm_36 (LSTM)               (None, 30, 128)           131584    \n",
      "_________________________________________________________________\n",
      "dropout_36 (Dropout)         (None, 30, 128)           0         \n",
      "_________________________________________________________________\n",
      "time_distributed_16 (TimeDis (None, 30, 1)             129       \n",
      "=================================================================\n",
      "Total params: 198,273\n",
      "Trainable params: 198,273\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#This is an LSTM A.E Model which tries to reconstruct the data considering it as sequential and the impermissible errors above a \n",
    "# certain threshold would be considered as anomalies \n",
    "model = tf.keras.Sequential()\n",
    "model.add(tf.keras.layers.LSTM(\n",
    "    units=128, \n",
    "    input_shape=(X_train.shape[1], X_train.shape[2])\n",
    "))\n",
    "model.add(tf.keras.layers.Dropout(rate=0.2))\n",
    "model.add(tf.keras.layers.RepeatVector(n=X_train.shape[1]))\n",
    "model.add(tf.keras.layers.LSTM(units=128, return_sequences=True))\n",
    "model.add(tf.keras.layers.Dropout(rate=0.2))\n",
    "model.add(tf.keras.layers.TimeDistributed(tf.keras.layers.Dense(units=X_train.shape[2])))\n",
    "model.compile(loss='mae', optimizer='adam')\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Neuixuq7FXPh"
   },
   "source": [
    "The RepeatVector layer simply repeats the input n times. Adding return_sequences=True in LSTM layer makes it return the sequence.\n",
    "\n",
    "Finally, the TimeDistributed layer creates a vector with a length of the number of outputs from the previous layer. This LSTM Autoencoder is ready for training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HhUPMxZtrFTS",
    "outputId": "fc070c66-3d41-4c76-a513-d4edd286661d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/7\n",
      "198/198 [==============================] - 4s 9ms/step - loss: 0.2309 - val_loss: 0.1936\n",
      "Epoch 2/7\n",
      "198/198 [==============================] - 1s 6ms/step - loss: 0.1790 - val_loss: 0.1629\n",
      "Epoch 3/7\n",
      "198/198 [==============================] - 1s 7ms/step - loss: 0.0804 - val_loss: 0.0968\n",
      "Epoch 4/7\n",
      "198/198 [==============================] - 1s 6ms/step - loss: 0.0766 - val_loss: 0.0494\n",
      "Epoch 5/7\n",
      "198/198 [==============================] - 1s 6ms/step - loss: 0.0922 - val_loss: 0.0344\n",
      "Epoch 6/7\n",
      "198/198 [==============================] - 1s 6ms/step - loss: 0.1020 - val_loss: 0.0259\n",
      "Epoch 7/7\n",
      "198/198 [==============================] - 1s 7ms/step - loss: 0.1282 - val_loss: 0.0292\n"
     ]
    }
   ],
   "source": [
    "#fitting of LSTM A.E Model on training set\n",
    "history = model.fit(X_train, y_train,epochs=7,batch_size=32,validation_split=0.1,shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 334
    },
    "id": "2GYE03C1TUv0",
    "outputId": "11ead21a-a981-4e8f-f614-c0a1252c0203"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the losses\n",
    "plt.plot(history.history['loss'], label='train')\n",
    "plt.plot(history.history['val_loss'], label='test')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eS1MkZiN43eE",
    "outputId": "7678d546-b32c-4ead-c99b-7b34dd4b7a80"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "55/55 [==============================] - 0s 3ms/step - loss: 0.0908\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.09076593816280365"
      ]
     },
     "execution_count": 199,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#evaluation of model's prediction on test data\n",
    "model.evaluate(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 369
    },
    "id": "DgOzzuc_xq8i",
    "outputId": "ad3cc798-9ae9-463c-9fe1-7e5fea96cee1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reconstruction error threshold: 0.6335980220865883\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train_pred = model.predict(X_train)\n",
    "train_mae_loss = np.mean(np.abs(X_train_pred - X_train), axis=1)\n",
    "plt.hist(train_mae_loss, bins=50)\n",
    "#plotting a histogram of training loss with number of samples\n",
    "plt.xlabel('Train MAE loss')\n",
    "plt.ylabel('Number of Samples');\n",
    "#threshold may be considered as the highest value of error in training data reconstruction \n",
    "threshold = np.max(train_mae_loss)\n",
    "print(f'Reconstruction error threshold: {threshold}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 352
    },
    "id": "G0IRFw03cP2V",
    "outputId": "852aaba0-5b9a-42c2-8c52-8119f7950cdc"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TVadOHX388ceqUqWKXeoHAAAozAr03riSdPLkyYf2BwQEZLn0ysOULl1akyZN0qRJk/JaGgAAQJGXq7B36dIlrV69WufPn9fNmzez3DbNZDJp8eLFNi0QAAAA1svVnr2RI0cqLS1Nnp6eKlWqlD3rAgAAgA0YDnvTpk1T+fLlNXPmTPn5+dmzJgAAANiI4YsqX7p0SX379iXoFXIpqekFXQIAAChEDK/sVaxYMdtbkaFw4TZoAADg9wyv7PXv319ff/217t69a896AAAAYEOGV/Z69uyphIQEderUSaGhoapQoYKcnZ2zjAsNDbVpgQAAALCe4bD366+/asuWLbp8+bLlrhd/ZDKZCHsAAACFiOGw9/777+vYsWMaMGCAAgMDufQK7C4lNV2uLo/eaWB0HAAAjyPDYW/v3r3q16+f3nvvPXvWA1hwsgkAAHlneDnE1dVVlStXtmctAAAAsDHDYe/555/Xnj177FkLAAAAbMxw2Bs7dqyuXLmiiRMn6sKFC1nuiwsAAIDCx/CevSZNmshkMunHH3/UsmXLsh1jMpl0/PhxmxUHAACAvDEc9kJDQ2UymexZCwAAAGzMcNj7xz/+Yc86AAAAYAdcnAwAAMCBGV7Z+73ExETduXNH6enpWfqeeuqpPBcFAAAA28hV2Pvuu+80Z84cnT17NscxsbGxeS4KAAAAtmH4MO7WrVs1evRopaWlqWfPnjKbzerYsaOCg4NVrFgx1a5dWyNGjLBnrQAAAMglw2Fv4cKFqlGjhqKiovTmm29Kkrp27arp06crIiJC586dU61atexWKAAAAHLPcNg7efKkQkND5ebmJienB0/L2LPn6+urHj16aN68efapEgAAAFYxHPbS09NVpkwZSZK7u7sk6c6dO5b+6tWr6/Tp0zYuDwAAAHlhOOyVK1dOly9flvQg7D3xxBP68ccfLf0//fSTihcvbvsKAQAAYDXDZ+M2bNhQe/fu1ciRIyVJbdq00eLFi+Xm5iaz2azly5erdevWdisUAAAAuWc47PXq1Utbt25VUlKS3N3dNWrUKB09elQzZ86UJNWsWVPvvfee3QoFAABA7hkOe3Xr1lXdunUtj729vRUVFaUTJ07I2dlZNWrUsJy4AQAAgMLBqjto/B6XWwEAACi8DIe93377TfHx8apRo4al7eLFi/rqq6908+ZNhYaG6rnnnrNLkQAAALCO4bA3adIknT9/XuHh4ZIe3B/31Vdf1fXr1yVJGzZs0OLFi/XMM8/Yp1IAAADkmuFNdocPH1arVq0sj9evX6/r169r3rx52rVrl2rUqKEFCxbYpUgAAABYx3DYu3Hjhp588knL4127dikgIEAtW7aUj4+PunTpouPHj9ulSAAAAFjHcNgrVqyYkpOTLY8PHDiQ6ZBtyZIldfPmTdtWB4uU1PSCLgEAABRBhvfsVa1aVZs2bdKrr76q7du369atW2ratKml/+rVqypdurRdioTk6uKkkLAjjxy3YXK9fKgGAAAUFYbD3quvvqqxY8fqmWeeUVJSkipVqpQp7B08eFB+fn52KRIAAADWMRz2QkNDJUnbtm2Tp6enhg0bJhcXF0kPLsty584d9erVyz5VAgAAwCq5uqhyaGioJfT9npeXlyIjI21WFAAAAGyD+5sBAAA4MMIeAACAAyPsAQAAODDCHgAAgAMj7AEAADiwHMNe27ZttW3bNsvjmTNn6tSpU/lSFAAAAGwjx7B35coVJSYmWh7PnDlTJ0+ezJeiAAAAYBs5hr1y5cplWckzmUx2LwjILaP3Deb+wgCAx1GOF1Vu27atFixYoF27dlnueTtnzhytXr06xxczmUxavHix7asEHoL7BgMAkLMcw96YMWNUqlQp7dmzR5cvX5bJZFJ8fLzu3buXn/UBAAAgD3IMe+7u7nrzzTf15ptvSpJq1aqlv/3tb3rxxRfzrTgAAADkjeFLr0yePFkNGjSwZy0AAACwsRxX9v6oS5culr//9ttv+uWXXyRJFStWlJeXl+0rAwAAQJ4ZDnuSdOLECU2cOFHR0dGZ2gMDAzVu3DjVqlUrV29+/fp1LVmyREeOHFFMTIzu3r2rJUuWqHHjxlnGbtu2TTNnztSZM2f0xBNPqFu3bho2bJiKFcv8EW7fvq2pU6dqy5YtSkpKUt26dRUWFiZ/f/9c1QYAAOAIDIe9U6dOqVevXkpJSVHbtm315z//WZJ05swZ7dixQ6+++qpWrlypmjVrGn7zc+fOaf78+apSpYr8/Px06NChbMf9+9//1ogRI9SkSRNNmDBBp06d0qxZs/Tbb79pwoQJlnHp6el6/fXXderUKQ0aNEheXl5avny5+vbtq8jISFWuXNlwbQAAAI7AcNj7/PPP5eLiohUrVmRZwTt16pT69Omjzz//XF988YXhN69du7b27dsnLy8vbd26VSNGjMh23JQpU/T0009r4cKFcnZ2liR5eHho3rx56tu3r6pWrSpJ2rhxow4dOqRZs2YpKChIkhQSEqIOHTpo5syZmjJliuHa4HhSUtPl6vLobaq1/APyoRoAAPKH4bD3ww8/qHfv3tkeqvX19VWvXr20cuXKXL25p6fnI8ecOXNGZ86c0YcffmgJepLUu3dvzZ07V5s3b9brr78uSdq0aZPKli2rtm3bWsZ5e3srJCRE3377rVJTU+Xi4pKrGuE4uB4fAOBxZPhs3Hv37snHxyfH/rJly9rlGnzHjx+XJAUEZF5tKVeunJ588klLvyTFxsaqdu3aWe70UadOHSUmJurChQs2rw8AAKAwMxz2KlWqpB07duTYv2PHDlWqVMkmRf1eXFycJGUbNH18fHT9+vVMY8uWLZtlXEbb78cCAAA8Dgwfxu3cubM+/fRTjR49WsOGDVP16tUlSWfPntWXX36p3bt3a/To0TYvMCkpSZLk6uqapc/NzS3TamJSUlK24zLaMl7LqJiYmGzb/3g2cn5o1KhRvr/n46wg5hj5izl2bMyv42OOjTMc9gYPHqzjx4/ru+++0/r16+Xk9GBRMD09XWazWSEhIRo0aJDNC3R3d5ckpaSkZOlLTk629GeMzW5cRtvvxxoREBAgNze3TG3R0dEEr8cAc+zY+Dl2bMyv42OOM0tOTs5xgUrKRdhzdnbWjBkztHv3bm3dutVyUeVKlSopKChIzZo1y3u12cg4fJvdIdq4uLhMd/X442HdDBlt2R3iBQAAcGS5uqiyJDVv3lzNmze3Ry3ZyrgYckxMjGrXrm1pv3btmq5evZrpYsm1atXSoUOHZDabM52kcfToUZUoUYLr7AEAgMeO4RM0CkrNmjVVvXp1rVq1Svfv37e0r1ixQk5OTmrfvr2lLTg4WNevX9e2bdssbfHx8dq4caPatm3LZVcAAMBjJ9cre7Y2e/ZsSQ9O9JCkqKgoRUdHq1SpUurTp48k6d1339Xw4cM1ePBgvfDCCzp16pSWLVumnj17qlq1apbX6tChg+rXr693333XcgeNFStWKD09XW+88Ub+fzgAAIACVuBh77PPPsv0OCIiQpJUoUIFS9hr3bq1Zs6cqZkzZ+rvf/+7vL29NXz4cP3lL3/J9FxnZ2fNmzdPU6ZM0dKlS5WcnKw6dero448/VpUqVfLnAwEAABQiBR72Tp48aWhcUFCQ5RZoD1O6dGlNmjRJkyZNymtpAAAARV6h37MHAAAA6xkKe0lJSVq7dq2OHHn0fUUBAABQeBgKe66urho/fnym+9ACAACg8DMU9pycnFS+fHklJCTYux4AAADYkOE9e6Ghofrmm2+yvR0ZAAAACifDZ+M2bNhQW7ZsUefOndW7d29VqVJFxYsXzzLumWeesWmBAAAAsJ7hsDdw4EDL3ydNmpTpdmSSLLcoi42NtV11AAAAyBPDYW/y5Mn2rAMAAAB2YDjsdenSxZ51AAAAwA64qDIAAIADy1XYu3LlisLCwtSyZUsFBARo7969kqT4+HiFhYXp6NGjdikSAAAA1jEc9i5evKiuXbtq8+bNqlmzpu7fv2/p8/b2VkxMjMLDw+1SJAAAAKxjeM/ejBkz5OTkpG+//VZubm5q1qxZpv5WrVppx44dNi8QAAAA1jO8srdnzx716tVL5cuXz3LZFUl66qmndPXqVZsWBwAAgLwxHPYSEhJUtmzZHPtTU1MzHdoFAABAwTMc9sqXL6/Tp0/n2H/kyBFVrlzZJkUBAADANgyHvXbt2ikiIkKnTp2ytGUczt20aZM2btyokJAQ21cIAAAAqxk+QWP48OHauXOnevToocDAQJlMJs2fP1/Tp0/X0aNH5e/vr0GDBtmzVgAAAOSS4ZU9T09PrVq1St26dVNMTIzMZrN2796tc+fOqXfv3lqyZInc3NzsWSsAAAByyfDKnvQg8I0fP17jx49XfHy8zGazvL29sz07FwAAAAUvV2Hv97y9vW1ZBwAAAOwg12Fv/fr12rp1qy5evChJqlSpkoKCgvTCCy/YvDgAAADkjeGwd/fuXY0YMUL79u2T2WxWqVKlJEnHjh3Thg0btGrVKs2ZM0clSpSwW7EAAADIHcMnaEyfPl179+5Vnz59tGvXLh04cEAHDhzQrl271KdPH+3fv1/Tp0+3Z60AAADIJcNhb8OGDQoODta4cePk4+Njaffx8dG4cePUvn17bdiwwS5FAgAAwDq5ul1a48aNc+xv0qSJEhISbFIUAAAAbMNw2PPz89PPP/+cY//PP/8sX19fmxQFAAAA2zAc9t566y2tXr1a27dvz9K3detWff311xo1apRNiwMAAEDe5Hg2blhYWJa2ihUrasSIEapWrZpq1KghSTp79qzOnTsnX19frVu3Tk2bNrVftQAAAMiVHMPemjVrcnzSTz/9pJ9++ilT28mTJ3Xq1Cl99NFHtqsOKAApqelydXn0orfRcQAAFKQcw96JEyfysw6g0HB1cVJI2JFHjtswuV4+VAMAQN6wLAEAAODACHsAAAAOLFf3xv3vf/+rZcuW6eeff9bNmzdlNpsz9ZtMJm3dutWmBQIAAMB6hsPe6tWr9f7778vFxUXVqlVT+fLl7VkXAAAAbMBw2Js7d678/f21YMECeXt727MmoEjgrF0AQFFgOOzduHFDgwcPJugB/x9n7QIAigLDyw01atTQ7du37VkLAAAAbMxw2Bs2bJiWL1+ua9eu2bMeAAAA2JDhw7jt27fXvXv31LFjR7Vt21YVKlSQk1PmrGgymTRixAibFwkAAADrGA57586d0+eff66EhARFRUVlO4awBwAAULgYDnsffPCB4uPjNW7cOAUGBqpUqVL2rAsAAAA2YDjsHT58WIMHD1bfvn3tWQ8AAABsyPAJGp6enlx2BQAAoIgxHPZCQkK0efNme9YCAAAAGzMc9l555RUlJibqL3/5i/bu3auLFy/q8uXLWf4AAACg8DC8Z69jx44ymUyKiYnRjh07chwXGxtrk8IAAACQd4bD3ogRI2QymexZCwAAAGzMcNh744037FkHAAAA7MDwnj0AAAAUPYZX9n744QdD45555hmriwEAAIBtGQ57ffv2NbRnzx4naOzfv1/9+vXLtm/9+vWqUaOG5fF///tfTZ06VcePH5enp6dCQkI0evRoFS9e3OZ1AUakpKbL1eXRi+hGxwEAkBuGw97kyZOztKWlpenixYuKjIxUxYoV1bNnT5sW90f9+/dX7dq1M7WVK1fO8vfY2FgNGDBAf/7znzV27FhdvXpV//znP/XLL79o7ty5dq0NyImri5NCwo48ctyGyfXyoRoAwOPGcNjr0qVLjn2DBw9+aL+tPPvsswoKCsqx/9NPP1WZMmW0dOlSeXh4SJIqVqyo8ePHa+/evWratKndawQAAChMbHLMqHTp0urevbsWLFhgi5d7qISEBKWlpWXbvmfPHoWGhlqCniR17txZJUqU0IYNG+xeGwAAQGFjeGXvUUqVKqWLFy/a6uWy9c477+ju3bsqVqyYGjdurPfee09+fn6SpJMnTyotLU0BAQGZnuPq6ip/f38u9gwAAB5LNgl7ycnJ+uabb/SnP/3JFi+XhYuLizp06KCWLVvKy8tLJ0+e1D//+U/17t1b4eHhqlatmuLi4iRJPj4+WZ7v4+Ojw4cP5/p9Y2Jism2Pjg8mRloAABmfSURBVI7O9WvlVaNGjfL9PZG/cnOCRuLdZJ2Izf6/TxhTED/HyD/Mr+Njjo0zHPbCwsKybb9165YOHz6s+Ph4vfvuuzYr7PcaNmyohg0bWh63bdtWbdq0UdeuXTVz5kx98sknSkpKkvRgJe+P3NzcLP25ERAQIDc3t0xt0dHRBC/YhdETOaQHJ3Pw36H1+Dl2bMyv42OOM0tOTs5xgUrKRdhbs2ZNtu2lS5dWtWrVFBYWphdffDH3FVqpVq1aatq0qfbt2ydJcnd3lySlpKRkGZucnGzpBwAAeJwYDnsnTpywZx1WKV++vCXsZRy+zTic+3txcXEqW7ZsvtYGAABQGBTpK7hevHhRXl5ekiRfX18VK1YsyzJmSkqKYmNj5e/vXxAlAgAAFKgiEfbi4+OztB08eFD79+9XixYtJEklS5ZU06ZNFRUVpcTERMu4qKgo3b17V8HBwflWLwAAQGHx0MO4w4YNy9WLmUwmzZkzJ08FZeett95S8eLF1aBBA3l5een06dNatWqVvLy89MYbb1jGjRo1Sq+88or69u2r7t276+rVq1q0aJFatmypZs2a2bwuoKBwCzYAgFEPDXs7d+7M1YsZuXeuNYKCgrRu3TotWrRICQkJ8vb2VqdOnfTGG2/oqaeesoyrXbu2Fi1apGnTpmny5Mny9PRUjx499Pbbb9ulLqCgcAs2AIBRDw17Rk7KOHDggKZOnapjx45le407W+jXr5/69etnaGxgYKBWrlxplzoAAACKGqsvqnzq1ClNmzZNu3btkoeHh0aOHKmBAwfasjYAAADkUa7D3pUrV/TZZ59p3bp1cnJyUt++fTV8+HDLWbEAAAAoPAyHvVu3bmnu3Llavny5UlJS1LFjR7311luqWLGiPesDAABAHjwy7KWkpOirr77SggULdPv2bTVv3lxjxozhunUAAABFwEPD3tdff62ZM2fq+vXrevrppzVmzBg1bdo0v2oDAABAHj007E2YMEEmk0kBAQEKCQnRiRMnHnqGrslk0oABA2xdIwAAAKz0yMO4ZrNZx44d07Fjxx75YoQ9AACAwuWhYW/JkiX5VQcAAADs4KFh79lnn82vOgAAAGAH3DQTAADAgRH2AAAAHBhhDwAAwIER9gAAABwYYQ8AAMCBEfYAAAAcGGEPAADAgRH2AAAAHBhhDwAAwIER9gAAABwYYa+ApaSmF3QJAADAgT303riwP1cXJ4WEHXnkuA2T6+VDNQAAwNGwsgcAAODACHsAAAAOjLAHAADgwAh7AAAADoywBwAA4MAIewAAAA6MsAcAAODACHuAAzN60W4u7g0AjouLKgMOjIt2AwBY2QMAAHBghD0AAAAHRtgDwN4+AHBg7NkDwN4+AHBgrOwBAAA4MMIeAACAAyPsATCMvX0AUPSwZw+AYeztA4Cih5U9AAAAB0bYAwAAcGCEPQAFhj2AAGB/7NkDYHMpqelydXn0vyXZAwgA9kfYA2BzhDgAKDw4jAsAAODACHsAAAAOjLAH4LHEySEAHhfs2QPwWGJfIYDHBSt7AAAADoywBwAA4MAIewAKvdzsm0s2MLZRo0Z5KQd2wj5KwD4cbs9eSkqKPvvsM0VFRen27duqVauWRo0apaZNmxZ0aQCsZHR/nfRgj11B7MUzeiFpo+Ns/b72eG9bYx8lYB8OF/bGjh2rzZs3q1+/fqpSpYrWrFmjIUOGaOnSpWrQoEFBlwegiLH13UCiPqxTIO8rOU7AzY2iUKMtOVL4h+04VNg7evSovvvuO4WFhWnAgAGSpNDQUHXq1EnTpk3TsmXLCrZAAEWOrVebHGn1qih8lqJQoy0VZPhH4eVQkX7jxo1ycXFR9+7dLW1ubm7q1q2boqOjdf369QKsDgCMY19a4fQ47it8HD+zo3Golb3Y2FhVq1ZNHh4emdrr1q0rs9ms2NhYlS1b9pGvYzabJT3Y/5ed5OTkvBf7O2VKmB85Jjk5+bEaVxRq5LspfOOKQo1Gx5nTU9Xr77GPHPfVu/6Gv5uExHtyKWbgkGZaulwNjJOM/f4y+r62er2AgIBMv6dtXaPRebHl/ypS09Jt+h1KBfeZjX6WR42z9f+Lc/PeGYz+rORm/qyRkVcy8ssfmcw59RRBnTp1Urly5bRw4cJM7WfOnFHHjh01ceLETKt+Oblz545OnTplrzIBAABsztfXVyVLlszS7lAre0lJSXJxccnS7ubmJsn4vwI8PDzk6+srFxcXmUwmm9YIAABgS2azWampqVmObGZwqLDn7u6u1NTULO0ZIS8j9D2Kk5NTtskYAACgMHJ3d8+xz6FO0PDx8cn2JIy4uDhJMrRfDwAAwJE4VNirVauWzp07p8TExEztR44csfQDAAA8Thwq7AUHBys1NVVff/21pS0lJUWRkZFq2LChypUrV4DVAQAA5D+H2rNXr149BQcHa9q0aYqLi1PlypW1Zs0aXb58WZMnTy7o8gAAAPKdQ116RXpwMsaMGTO0bt063bp1S35+fnr77bfVrFmzgi4NAAAg3zlc2AMAAMD/cag9ewAAAMiMsAcAAODACHt6cMbu1KlT1aJFC9WtW1c9evTQ3r17DT332rVrGjlypAIDA9WwYUP95S9/0cWLF+1cMXLL2jnevHmz3nrrLbVp08ZyAtDHH3+sO3fu5EPVyI28/Bz/3pAhQ+Tn56dJkybZoUpYK6/zu27dOnXr1k3169fXs88+qz59+ujo0aN2rBi5lZc53rNnj/r27avGjRvrmWeeUc+ePbV+/Xo7V1x0EPYkjR07VosXL9ZLL72kcePGycnJSUOGDNGhQ4ce+rzExET169dP0dHRGjZsmN58800dP35c/fr1061bt/Kpehhh7RxPmDBBZ8+eVefOnTV+/Hi1aNFCS5cuVa9evex6E27knrVz/Hs7d+7UwYMH7VglrJWX+Z0+fbrGjh2rmjVraty4cRoxYoQqVapkueA+Cgdr53jHjh0aNGiQ0tLS9MYbb2jkyJFycnLSqFGjMl2K7bFmfswdOXLE7Ovra160aJGlLSkpyRwUFGTu3bv3Q587b948s5+fn/nHH3+0tJ05c8bs7+9vnjFjhr1KRi7lZY737duXpW3NmjVmX19fc0REhK1LhZXyMscZkpOTze3btzd/8cUXZl9fX/PEiRPtVC1yKy/zGx0dbfbz8zNv3rzZzlUiL/Iyx4MHDza3aNHCnJycbGlLTk42t2jRwvzqq6/aq+Qi5bFf2du4caNcXFzUvXt3S5ubm5u6deum6OjobG+/lmHTpk2qX7++nn76aUtbjRo11LRpU23YsMGudcO4vMxx48aNs7QFBQVJks6ePWv7YmGVvMxxhiVLligpKUmDBw+2Z6mwQl7md8mSJapTp47atWun9PT0LHdYQuGQlzlOSEhQ6dKl5erqamlzdXVV6dKl5ebmZte6i4rHPuzFxsaqWrVq8vDwyNRet25dmc1mxcbGZvu89PR0nTx5UgEBAVn66tSpo/Pnz+vevXt2qRm5Y+0c5+TXX3+VJHl5edmsRuRNXuc4Li5Os2fP1qhRo1S8eHF7lgor5GV+9+7dqzp16ujTTz9Vo0aN1LBhQ7Vp00bffPONvctGLuRljp999lmdPn1aM2bM0IULF3ThwgXNmDFD58+f16BBg+xdepHgUHfQsEZcXFy2t1Hz8fGRpBz/NXHz5k2lpKRYxv3xuWaz2XIXDxQsa+c4J/Pnz5ezs7Pat29vk/qQd3md408//VTVqlVT586d7VIf8sba+b1165Zu3ryp7777Ts7OzhozZozKlCmjZcuW6Z133lHx4sXVrl07u9YOY/LyMzxs2DBduHBBc+fO1Zw5cyRJJUqU0OzZs9W8eXP7FFzEPPZhLykpSS4uLlnaM5Z+c9qEn9H++2XjPz43KSnJVmUiD6yd4+ysW7dO4eHhGjp0KEG+EMnLHB89elRr167V0qVLZTKZ7FYjrGft/N69e1fSg3+cr169WvXq1ZMktWvXTu3atdOsWbMIe4VEXn6GXV1dVbVqVQUHB6tdu3a6f/++Vq9erbfeektfffWV6tata7e6i4rHPuy5u7srNTU1S3vGf1g5He/PaE9JScnxue7u7rYqE3lg7Rz/0cGDBzVu3Dg9//zzGjlypE1rRN5YO8dms1mTJk1S+/btFRgYaNcaYb28/p6uWLGiJehJD8JBhw4dtGTJEiUmJmY5dIj8l5ff03//+9917NgxhYeHy8npwe60kJAQderUSR999JFWrlxpn6KLkMd+z56Pj0+2y8MZp+SXLVs22+eVKVNGrq6u2Z66HxcXJ5PJlO0hXuQ/a+f4906cOKHhw4fLz89P06dPl7Ozs83rhPWsneMtW7bo6NGj6tWrl3755RfLH+nBpu9ffvmFFfpCIK+/p//0pz9l6fvTn/4ks9mshIQE2xYLq1g7xykpKQoPD9fzzz9vCXqS5OLioueee07Hjh1TWlqafYouQh77sFerVi2dO3cuyxlaR44csfRnx8nJSb6+voqJicnSd/ToUVWpUoWN3oWEtXOc4cKFC3rttdfk7e2tL7/8UiVKlLBbrbCOtXN8+fJlpaenq3///mrbtq3ljyRFRkaqbdu2OnDggH2LxyPl5fe0v7+/rl27lqXv6tWrcnZ2VunSpW1fMHLN2jm+efOm0tLSdP/+/Sx9aWlpSktLk9lstn3BRcxjH/aCg4OVmpqa6cKLKSkpioyMVMOGDS0bRi9fvpzlUhsdOnTQ4cOHdfz4cUvbTz/9pH379ik4ODh/PgAeKS9zHBcXp0GDBslkMmnhwoXy9vbO19phjLVz3KZNG82aNSvLH0lq3bq1Zs2apdq1a+fvh0EWefkZDg4O1pUrV7R7925LW0JCgjZs2KAGDRqw3aaQsHaOn3jiCZUqVUpbtmzJdBg4MTFRO3bskK+vb7Z7AR83JjORVyNHjtS2bdvUv39/Va5cWWvWrFFMTIwWL16sRo0aSZL69u2rAwcO6OTJk5bnJSQkqEuXLrp3754GDhwoZ2dnffXVVzKbzVq7di2X5ihErJ3jzp0768SJE3rttdfk6+ub6TUrV66sBg0a5OvnQM6snePs+Pn5qV+/fho3blx+lA4DrJ3fe/fu6eWXX9a1a9c0YMAAlSpVShERETp37lym56LgWTvHc+bM0YwZM1S7dm299NJLSk9PV3h4uM6ePavp06frhRdeKKiPVGg89idoSNKUKVM0Y8YMRUVF6datW/Lz89O8efMe+UvA09NTS5cu1UcffaTZs2crPT1djRs31rhx4wh6hYy1c3zixAlJ0oIFC7L0denShbBXiFg7xygarJ3f4sWLa8mSJZoyZYr+9a9/KSkpSbVr19aiRYv4b6OQsXaOhw8frooVK2rJkiWaNWuWUlJS5Ofnp5kzZ3K29f/Hyh4AAIADe+z37AEAADgywh4AAIADI+wBAAA4MMIeAACAAyPsAQAAODDCHgAAgAMj7AEAADgwwh4AODA/Pz+NHTu2oMsAUIC4gwaAAuXn52d47LZt21SxYkWbvG9kZKRu376tAQMGGH5OmzZtdOnSJZUpU0a7du2Sq6trljHDhw/X9u3bH1rv2bNnLbdwWrZsmQIDA7N9v0d9Nw97LgBkIOwBKFBTpkzJ9Dg6OlqrVq1Sz549s9wmydvb22bvu2bNGl26dClXYU+S3NzcdPPmTW3btk0hISGZ+n799Vd9//33cnNzU3Jyco6vER4eLg8PD7m7uysiIuKhgc3f318DBw7Mtq969eq5qh3A44mwB6BAde7cOdPj+/fva9WqVapfv36WvsKgcuXKMplMioyMzBL21q5dK0lq3bq1Nm7cmO3zU1NTFRUVpeDgYJUsWVKrV6/WuHHj5Onpme34cuXKFcrvAUDRwZ49AEWC2WzW8uXL9fLLL6tevXpq0KCB+vbtq3379mUZu3btWnXr1k2BgYGqX7++2rZtq9GjRys+Pl7Sg8OxBw4c0KVLl+Tn52f5s3//fkO1dO3aVbt379a1a9cytUdGRqpVq1Z64okncnzujh07dOPGDXXp0kVdunTR3bt3tWHDhlx8E7bx9ddfq0uXLqpbt64aNWqkQYMG6eDBg1nG7dy5U3369FHjxo1Vt25dPf/88/rrX/+qc+fOWcZcuXJFYWFhat26tQICAtS0aVO98sorWrNmTX5+JAA5YGUPQJHwzjvv6LvvvlOHDh308ssvKyUlRevWrdOgQYP0xRdfqG3btpIeBL333ntPgYGBevPNN+Xu7q4rV67o3//+t27cuCFvb2/97W9/0yeffKLffvtNYWFhlveoUaOGoVpeeuklTZs2TWvXrtXQoUMlSYcPH9bZs2c1evRo7d69O8fnhoeHq2LFigoMDJTJZNLTTz+tiIgIde/ePdvxaWlplpD6eyaTSV5eXobq/aOpU6dqwYIFqlu3rt5++20lJCRo9erV6t+/v2bPnq1WrVpJkg4cOKDhw4erZs2aGjp0qEqWLKnr169r7969unDhgqpVq6a0tDQNHDhQ165dU+/evVW1alUlJCTo5MmTOnjwoLp06WJVjQBsh7AHoNDbsmWL1q1bpw8//FA9e/a0tPfr1089evTQpEmT1KZNG5lMJm3dulUeHh5avHixihX7v19xI0eOtPw9KChIixcvVnJyslWHSL29vdW6dWtFRkZawl5ERIR8fHzUqlWrHMPetWvX9J///EfDhw+XyWSSJIWGhuqjjz7S2bNnsw2b//nPf9S0adMs7SVKlNChQ4dyXftPP/2khQsXqmHDhlq8eLHlJJPu3burY8eO+uCDD7RlyxY5Oztr27ZtSk9P16JFizKtVo4YMcLy9zNnzujcuXMaM2aMhgwZkut6ANgfh3EBFHrffPONPDw8FBQUpPj4eMuf27dvW86QPX/+vCSpZMmSSkpK0s6dO2U2m+1WU9euXXX+/HlFR0crKSlJ69ev10svvZQpYP7RmjVrlJ6ertDQUEvbiy++KBcXF4WHh2f7nHr16mnRokVZ/sydO9equrdt2yaz2azXXnst09nE5cqV08svv6xLly7p+PHjkh58l5K0adMmpaWlZft6GWP279+vGzduWFUTAPtiZQ9AoXf27FklJiaqWbNmOY65ceOGqlWrpqFDh+qHH37QiBEjVKZMGT377LNq2bKlQkJCcjwJwhrPPfecfHx8FBkZqYsXLyohIUFdu3bNcbzZbFZERIT8/PyUnp6un3/+2dLXoEEDffPNNxo9enSWsOjl5fXQz51bv/zyiySpZs2aWfoy2i5evKg6dero1Vdf1bZt2/TBBx9o2rRpatSokZ577jl16tTJcmZ0hQoVNGzYMM2bN08tWrSQv7+/mjRpouDgYNWtW9dmdQOwHmEPQKFnNpvl7e2tTz75JMcxGUGlatWqWr9+vfbu3au9e/fqwIEDGj9+vD7//HMtW7ZMlStXtklNzs7OCg0N1fLly3XmzBnVr1//oXv+Dhw4oAsXLkiS2rdvn+2YnTt3KigoyCb12YKXl5fCw8N18OBB7dmzRz/88IMmT56sL774QvPmzVODBg0kSaNGjVK3bt20c+dOHTx4UOHh4Vq4cKFee+01vfPOOwX8KQAQ9gAUelWqVNH58+dVr149eXh4PHK8q6urWrVqZTnR4N///rdef/11LVq0SO+//77N6uratavmz5+vw4cP6+9///tDx0ZERMjV1VUff/yxnJyy7qB5//33FR4ebvewV6lSJUnS6dOnswTfM2fOZBojPQi1jRs3VuPGjSVJJ06cUNeuXTVnzhzNmzcv0+v27dtXffv2VXJysgYPHqwFCxZo0KBBDz07GYD9sWcPQKEXGhqq9PR0ffrpp9n2//rrr5a/Z3fm6tNPPy1JunXrlqXNw8NDt27dytO+vmrVqmncuHH661//arkjRnbu3LmjTZs2qXnz5nrhhRcUHByc5U+bNm30/fff6/r161bXY0TGiSwLFy5Uamqqpf369euKjIxUhQoVLN9Xdt9l9erV5ebmZvku79y5k+l1pAcXns644PPvv3MABYOVPQCFXnBwsF5++WX961//0o8//qjWrVvLy8tLV69e1eHDh/Xzzz9r27ZtkqTBgwerZMmSCgwMVPny5XX79m2tWbNGJpMp05m39erV044dO/Thhx+qQYMGcnZ2VpMmTXK9CtWvX79Hjvn222+VlJSkDh065Dimffv2ioyM1Nq1a/X6669b2q9du6aoqKhsn9OgQYNcH5auXr26ZdWtT58+CgkJUWJiolavXq27d+9q2rRpcnZ2liRNmDBBV69eVYsWLfTUU08pKSlJGzZsUGJiouW73L9/vyZMmKD27durWrVq8vDwUExMjMLDw1WvXj3u8gEUAoQ9AEXC5MmT1bhxY61evVpffvmlUlNT5ePjo6efflqjR4+2jOvVq5c2bNigVatW6datWypTpoz8/f01fvx4NWnSxDJuwIABunjxojZt2qSVK1cqPT1dS5Ysscshx/DwcBUrVkxt2rTJcUzz5s3l4eGhiIiITGEvNjZW7777brbPmThxolV7EN955x1VqVJFy5cv1yeffCIXFxfVq1dPn3zySaZbt3Xu3FmRkZFas2aN4uPj5enpqT//+c/6/PPPLcHVz89P7dq104EDB7Ru3Tqlp6erfPnyGjp0qAYNGpTr2gDYnslsz2sTAAAAoECxZw8AAMCBEfYAAAAcGGEPAADAgRH2AAAAHBhhDwAAwIER9gAAABwYYQ8AAMCBEfYAAAAcGGEPAADAgRH2AAAAHNj/A5m1jDl4JDwdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting a histogram of testing data loss with number of samples\n",
    "X_test_pred = model.predict(X_test, verbose=0)\n",
    "test_mae_loss = np.mean(np.abs(X_test_pred-X_test), axis=1)\n",
    "plt.hist(test_mae_loss, bins=50)\n",
    "plt.xlabel('Test MAE loss')\n",
    "plt.ylabel('Number of samples');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "U-87Uma2uEg6"
   },
   "outputs": [],
   "source": [
    "TIME_STEPS=30\n",
    "#creating a new dataframe for test data which indicates where its an anomaly or not\n",
    "test_score_df = pd.DataFrame(test[TIME_STEPS:])\n",
    "test_score_df['loss'] = test_mae_loss\n",
    "test_score_df['threshold'] = threshold\n",
    "test_score_df['anomaly'] = test_score_df['loss'] > test_score_df['threshold']\n",
    "test_score_df['Close'] = test[TIME_STEPS:]['Close']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 359
    },
    "id": "wy1c39sc6IWH",
    "outputId": "d788c6c2-ad67-43bf-f78d-d4f4e59b24af"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Close</th>\n",
       "      <th>loss</th>\n",
       "      <th>threshold</th>\n",
       "      <th>anomaly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7089</th>\n",
       "      <td>2013-10-16</td>\n",
       "      <td>2.162041</td>\n",
       "      <td>0.121652</td>\n",
       "      <td>0.633598</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7090</th>\n",
       "      <td>2013-10-17</td>\n",
       "      <td>2.196819</td>\n",
       "      <td>0.151654</td>\n",
       "      <td>0.633598</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7091</th>\n",
       "      <td>2013-10-18</td>\n",
       "      <td>2.183069</td>\n",
       "      <td>0.181699</td>\n",
       "      <td>0.633598</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7092</th>\n",
       "      <td>2013-10-21</td>\n",
       "      <td>2.165680</td>\n",
       "      <td>0.188551</td>\n",
       "      <td>0.633598</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7093</th>\n",
       "      <td>2013-10-22</td>\n",
       "      <td>2.212591</td>\n",
       "      <td>0.179040</td>\n",
       "      <td>0.633598</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7094</th>\n",
       "      <td>2013-10-23</td>\n",
       "      <td>2.202076</td>\n",
       "      <td>0.191112</td>\n",
       "      <td>0.633598</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7095</th>\n",
       "      <td>2013-10-24</td>\n",
       "      <td>2.212186</td>\n",
       "      <td>0.191970</td>\n",
       "      <td>0.633598</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7096</th>\n",
       "      <td>2013-10-25</td>\n",
       "      <td>2.201672</td>\n",
       "      <td>0.193393</td>\n",
       "      <td>0.633598</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7097</th>\n",
       "      <td>2013-10-28</td>\n",
       "      <td>2.213804</td>\n",
       "      <td>0.186030</td>\n",
       "      <td>0.633598</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7098</th>\n",
       "      <td>2013-10-29</td>\n",
       "      <td>2.244134</td>\n",
       "      <td>0.184665</td>\n",
       "      <td>0.633598</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date     Close      loss  threshold  anomaly\n",
       "7089 2013-10-16  2.162041  0.121652   0.633598    False\n",
       "7090 2013-10-17  2.196819  0.151654   0.633598    False\n",
       "7091 2013-10-18  2.183069  0.181699   0.633598    False\n",
       "7092 2013-10-21  2.165680  0.188551   0.633598    False\n",
       "7093 2013-10-22  2.212591  0.179040   0.633598    False\n",
       "7094 2013-10-23  2.202076  0.191112   0.633598    False\n",
       "7095 2013-10-24  2.212186  0.191970   0.633598    False\n",
       "7096 2013-10-25  2.201672  0.193393   0.633598    False\n",
       "7097 2013-10-28  2.213804  0.186030   0.633598    False\n",
       "7098 2013-10-29  2.244134  0.184665   0.633598    False"
      ]
     },
     "execution_count": 205,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_score_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 542
    },
    "id": "qWiYvGmkv_Pb",
    "outputId": "13eab531-588c-4b4d-da9b-44543fd8c899"
   },
   "outputs": [
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\"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Test loss vs. Threshold\"}},\n",
       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('1dc068bb-1517-4bce-ab2a-8b9993b7d695');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                \n",
       "            </script>\n",
       "        </div>\n",
       "</body>\n",
       "</html>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#A plotted figure of test loss vs threshold\n",
    "fig = go.Figure()\n",
    "fig.add_trace(go.Scatter(x=test_score_df['Date'], y=test_score_df['loss'], name='Test loss'))\n",
    "fig.add_trace(go.Scatter(x=test_score_df['Date'], y=test_score_df['threshold'], name='Threshold'))\n",
    "fig.update_layout(showlegend=True, title='Test loss vs. Threshold')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "EkVxvoAt0Egc",
    "outputId": "d5216e26-c4a3-4434-ef5f-ededcfa50afd"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Close</th>\n",
       "      <th>loss</th>\n",
       "      <th>threshold</th>\n",
       "      <th>anomaly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8396</th>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>3.601706</td>\n",
       "      <td>0.647581</td>\n",
       "      <td>0.633598</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8708</th>\n",
       "      <td>2020-03-24</td>\n",
       "      <td>3.297193</td>\n",
       "      <td>0.872714</td>\n",
       "      <td>0.633598</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8709</th>\n",
       "      <td>2020-03-25</td>\n",
       "      <td>3.306089</td>\n",
       "      <td>0.851107</td>\n",
       "      <td>0.633598</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8710</th>\n",
       "      <td>2020-03-26</td>\n",
       "      <td>3.596044</td>\n",
       "      <td>0.787606</td>\n",
       "      <td>0.633598</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8725</th>\n",
       "      <td>2020-04-17</td>\n",
       "      <td>4.625244</td>\n",
       "      <td>0.634052</td>\n",
       "      <td>0.633598</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date     Close      loss  threshold  anomaly\n",
       "8396 2018-12-26  3.601706  0.647581   0.633598     True\n",
       "8708 2020-03-24  3.297193  0.872714   0.633598     True\n",
       "8709 2020-03-25  3.306089  0.851107   0.633598     True\n",
       "8710 2020-03-26  3.596044  0.787606   0.633598     True\n",
       "8725 2020-04-17  4.625244  0.634052   0.633598     True"
      ]
     },
     "execution_count": 207,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#anomalies is a dataframe consisting of anomalous test data\n",
    "anomalies = test_score_df[test_score_df.anomaly == True]\n",
    "anomalies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DI4ZG3Ay6llz",
    "outputId": "4c023cc4-b0f5-428d-bfe2-bac707f70ca7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13, 5)"
      ]
     },
     "execution_count": 208,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#below is the number of anomalous examples\n",
    "anomalies.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 434
    },
    "id": "b9pidLApzimf",
    "outputId": "7d89bcd0-430a-41cc-87de-9c692a59aade"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning:\n",
      "\n",
      "Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the test data displaying anomalous data with red dots\n",
    "plt.plot(test[TIME_STEPS:].index, scaler.inverse_transform(test[TIME_STEPS:].Close), label='close price');\n",
    "sns.scatterplot(anomalies.index,scaler.inverse_transform(anomalies.Close),color=sns.color_palette()[3],label='anomaly')\n",
    "plt.xticks(rotation=25)\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 542
    },
    "id": "73hl1uWx7egN",
    "outputId": "29d18d91-2aea-4e26-f494-94436ed5ae52"
   },
   "outputs": [
    {
     "data": {
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       "</html>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#A plot displaying closing prices Detected Anomalies with the Date  \n",
    "fig = go.Figure()\n",
    "fig.add_trace(go.Scatter(x=test_score_df['Date'], y=scaler.inverse_transform(test_score_df['Close']), name='Close price'))\n",
    "fig.add_trace(go.Scatter(x=anomalies['Date'], y=scaler.inverse_transform(anomalies['Close']), mode='markers', name='Anomaly'))\n",
    "fig.update_layout(showlegend=True, title='Detected anomalies')\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vTymVsL77--3"
   },
   "source": [
    "The model found that some low price anomalies in March and high price anomalies in April.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "muiVUGVk7z5u"
   },
   "outputs": [],
   "source": []
  }
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